1. Field of the Invention
The present invention relates to a machine learning unit able to judge the necessity of spindle replacement. The present invention further relates to a spindle replacement judging device able to judge the necessity of spindle replacement. The present invention further relates to a controller able to judge the necessity of spindle replacement. The present invention further relates to a machine tool able to judge the necessity of spindle replacement. The present invention further relates to a production system able to judge the necessity of spindle replacement. The present invention further relates to a machine learning method able to judge the necessity of spindle replacement.
2. Description of the Related Art
Abnormal operation (e.g., vibration, uneven rotation, etc.) of a spindle of a machine tool (e.g., a machining center, a lathe, etc.) would have a direct impact on the processing accuracy of a workpiece, and therefore, when the extent of the abnormal operation exceeds an allowable range, it is required to replace the spindle with a new one. Conventionally, an operator has judged, according to an experimental rule, whether or not the extent of the abnormal operation exceeds an allowable range, based on the operator's sensing of abnormal noise or vibration generated during operation of the spindle or the measurement of a difference between an actual dimension of a processed workpiece which has been processed using the spindle and a dimension target value. On the other hand, as described in, e.g., Japanese Unexamined Patent Publication No. H5-052712 A (JP H5-052712 A), a malfunction predicting system for production machinery, which compares a vibration pattern as a detected value of a vibration sensor provided in the production machinery with a predetermined reference vibration pattern in a normal condition so as to predict the malfunction of the production machinery, has been known.